Simplified compositional models for calculating properties of mixed fluids in a common surface network

ABSTRACT

System and methods of simulating fluid production in a multi-reservoir system with a common surface network are presented. An equation of state (EOS) characterization of fluids is matched with a delumped EOS model representing different components of the fluids for each reservoir within the multi-reservoir system. Fluid production in the multi-reservoir system is simulated for at least one simulation point in the common surface network, based in part on the delumped EOS model for each reservoir. If the fluids produced during the simulation at the simulation point are mixed fluids from different reservoirs, one or more interpolation tables representing the mixed fluids are generated and properties of the mixed fluids are calculated based on the generated interpolation tables. Otherwise, the properties of the fluids are calculated using the delumped EOS model corresponding to the reservoir from which the fluids are produced.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a U.S. National Stage patent application ofInternational Patent Application No. PCT/US2015/020298, filed on Mar.12, 2015, which claims the benefit of U.S. Provisional PatentApplication No. 61/951,825, filed on Mar. 12, 2014, titled “Procedurefor Using Simplified Compositional Models for Calculating Properties ofMixed Fluids in a Common Surface Network,” the benefit of both of whichare claimed and the disclosure of both of which are incorporated hereinby reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to the recovery of subterraneandeposits and more specifically to the recovery of subterraneanhydrocarbon deposits from multiple reservoirs through a common surfacenetwork.

BACKGROUND

When multiple reservoirs are produced through a common facility network,the capability to integrate the modeling of surface and subsurface canbe critical to field development and optimization. The shared facilitynetwork imposes constraints that the combined production cannot exceed,determines the pressure drop in the flow lines, and the composition andvolume of the sales and reinjection streams. Pressure drop in flow linesis particularly important in deepwater field development, where flowlines are long, and production from multiple reservoirs can flow throughthe same riser.

Often, the fluid characterizations of these reservoirs have been derivedindependently. In each case, the appropriate fluid representation wasselected that provided an optimum combination of accuracy andcomputational efficiency. The two most common fluid characterizationsare the equation of state (EOS) and the black oil model.

A hydrocarbon fluid may actually be composed of hundreds of distinctcomponents. When modeling using an EOS, the engineer must specify thenumber of pseudo-components (typically 5 to 12) and their EOSproperties. Pseudo-components are combinations of actual components.Alternatively, black-oil modeling involves specification of a number ofcommon engineering measurements in tables that vary with pressure.However, it is inherently a model with two pseudo-components. The netresult is that the different connected reservoirs are being modeled witha variable number of pseudo-components, some of which may be common.However, even the common pseudo-components may have different fluidproperties in the different reservoirs.

BRIEF DESCRIPTION OF THE DRAWINGS

Illustrative embodiments of the present disclosure are described indetail below with reference to the attached drawing figures.

FIGS. 1A and 1B illustrate examples of production wells suitable forhydrocarbon production and exploration from a subsurface reservoir.

FIG. 2 is a block diagram of an exemplary system for simulating fluidproduction in a multi-reservoir system with a common surface network.

FIG. 3 is a diagram illustrating an exemplary of a multi-reservoirsystem with a common surface network.

FIG. 4 is a flowchart of an exemplary method of using EOS compositionalmodels to simulate fluid production and calculate properties of fluidsproduced in a multi-reservoir system with a common surface network.

FIG. 5 is a block diagram of an exemplary computer system in whichembodiments of the present disclosure may be implemented.

The illustrated figures are only exemplary and are not intended toassert or imply any limitation with regard to the environment,architecture, design, or process in which different embodiments may beimplemented.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Embodiments of the present disclosure relate to using simplifiedcompositional models to simulate fluid production and calculateproperties of mixed fluids produced in a multi-reservoir system with acommon surface network. While the present disclosure is described hereinwith reference to illustrative embodiments for particular applications,it should be understood that embodiments are not limited thereto. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the embodiments in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theillustrative embodiments described herein are provided to explain theprinciples of the disclosure and the practical application thereof, andto enable others of ordinary skill in the art to understand that thedisclosed embodiments may be modified as desired for a particularimplementation or use. The scope of the claims is intended to broadlycover the disclosed embodiments and any such modification. Any actualdata values listed in the detailed description are provided forillustrative purposes only and embodiments of the present disclosure arenot intended to be limited thereto. Thus, the operational behavior ofembodiments will be described with the understanding that modificationsand variations of the embodiments are possible, given the level ofdetail presented herein.

In the detailed description herein, references to “one embodiment,” “anembodiment,” “an example embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may not necessarily include theparticular feature, structure, or characteristic. Moreover, such phrasesare not necessarily referring to the same embodiment. Further, when aparticular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to implement such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise”and/or “comprising,” when used in this specification and/or the claims,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. The correspondingstructures, materials, acts, and equivalents of all means or step plusfunction elements in the claims below are intended to include anystructure, material, or act for performing the function in combinationwith other claimed elements as specifically claimed.

The disclosed embodiments and advantages thereof are best understood byreferring to the drawings, in which like numerals are used for like andcorresponding parts of the various drawings. Other features andadvantages of the disclosed embodiments will be or will become apparentto one of ordinary skill in the art upon examination of the followingfigures and detailed description. It is intended that all suchadditional features and advantages be included within the scope of thedisclosed embodiments. Further, the illustrated figures are onlyexemplary and are not intended to assert or imply any limitation withregard to the environment, architecture, design, or process in whichdifferent embodiments may be implemented.

The disclosed embodiments relate to using equation of state (EOS)compositional models and/or black oil models to simulate fluidproduction in a multi-reservoir system with a common surface network. Aswill be described in further detail below, reservoir fluids frommultiple hydrocarbon reservoirs may be produced through a commongathering point or shared facility of the common surface network. Thus,heterogeneous fluids from different reservoirs that flow into the commongathering point may combine or mix together. In an example, thedisclosed embodiments may be used to calculate properties of the mixedfluids at the common gathering point or other points within the commonsurface network during a simulation of fluid production in themulti-reservoir system. One example of a reservoir simulator in whichthe disclosed embodiments may be implemented is the Nexus® integratedreservoir and surface simulator available from Landmark GraphicsCorporation of Houston, Tex.

In an embodiment, the simulation may be based in part on productionsystem data including various measurements collected downhole from awell drilled within each hydrocarbon reservoir, e.g., in the form of aproduction well for an oil and gas reservoir. Further, multipleproduction wells may be drilled for providing access to the reservoirfluids underground. Measured well data may be collected regularly fromeach production well to track changing conditions in the reservoir, aswill be described in further detail below with respect to the productionwell examples illustrated in FIGS. 1A and 1B.

FIG. 1A is a diagram of an exemplary production well 100A with aborehole 102 that has been drilled into a reservoir formation. Borehole102 may be drilled to any depth and in any direction within theformation. For example, borehole 102 may be drilled to ten thousand feetor more in depth and further, may be steered horizontally for anydistance through the formation, as desired for a particularimplementation. The production well 100A also includes a casing header104 and a casing 106, both secured into place by cement 103. A blowoutpreventer (BOP) 108 couples to casing header 104 and a productionwellhead 110, which together seal in the well head and enable fluids tobe extracted from the well in a safe and controlled manner.

Measured well data may be periodically sampled and collected from theproduction well 100A and combined with measurements from other wellswithin a reservoir, enabling the overall state of the reservoir to bemonitored and assessed. These measurements may be taken using a numberof different downhole and surface instruments, including but not limitedto, a temperature and pressure sensor 118 and a flow meter 120.Additional devices may also be coupled in-line to a production tubing112 including, for example, a downhole choke 116 (e.g., for varying alevel of fluid flow restriction), an electric submersible pump (ESP) 122(e.g., for drawing in fluid flowing from perforations 125 outside ESP122 and production tubing 112), an ESP motor 124 (e.g., for driving ESP122), and a packer 114 (e.g., for isolating the production zone belowthe packer from the rest of well 100A). Additional surface measurementdevices may be used to measure, for example, the tubing head pressureand the electrical power consumption of ESP motor 124.

FIG. 1B is a diagram showing an alternative embodiment of the productionwell 100A of FIG. 1A, which includes many of the same components as well100A but has been adapted for artificial gas lift. As shown in FIG. 1B,a production well 100B further includes a gas lift injector mandrel 126in addition to the above-described components of well 100A. In anembodiment, gas lift injector mandrel 126 is coupled in-line withproduction tubing 112 for controlling a flow of injected gas into aportion of production tubing 112 located above-ground or at the surfaceof the well near wellhead 110. Although not shown in FIG. 1B, the gaslift production well 100B may also include the same type of downhole andsurface instruments as shown for production well 100A in FIG. 1A forproviding the above-described measurements.

As shown in FIGS. 1A and 1B, each of the devices along production tubing112 couples to a cable 128, which may be attached to an exterior portionof production tubing 112. Cable 128 may be used primarily to providepower to the devices to which it couples. Cable 128 also may be used toprovide signal paths (e.g., electrical or optical paths), through whichcontrol signals may be directed from the surface to the downhole devicesas well as telemetry signals from the downhole devices to the surface.The respective control and telemetry signals may be sent and received bya control unit 132 at the surface of the production well. Control unit132 may be coupled to cable 128 through blowout preventer 108. In anembodiment, field personnel may use control unit 132 to control andmonitor the downhole devices locally, e.g., via a user interfaceprovided at a terminal or control panel integrated with control unit132. Additionally or alternatively, the downhole devices may becontrolled and monitored by a remote processing system 140. Processingsystem 140 may be used to provide various supervisory control and dataacquisition (SCADA) functionality for the production wells associatedwith each reservoir in a field. For example, a remote operator may useprocessing system 140 to send appropriate commands for controllingwellsite operations to control unit 132. Communication between controlunit 132 and processing system 140 may be via one or more communicationnetworks, e.g., in the form of a wireless network (e.g., a cellularnetwork), a wired network (e.g., a cabled connection to the Internet) ora combination of wireless and wired networks.

As shown in FIGS. 1A and 1B, processing system 140 may include acomputing device 142 (e.g., a server) and a data storage device 144(e.g., a database). Although only one computing device and one datastorage device are shown in FIGS. 1A and 1B, it should be appreciatedthat processing system 140 may include additional computing devices anddata storage devices. Computing device 142 may be implemented using anytype of computing device having at least one processor, a memory and anetworking interface capable of sending and receiving data to and fromcontrol unit 132 via a communication network. In an embodiment,computing device 142 may be a type of server. Examples of such a serverinclude, but are not limited to, a web server, an application server, aproxy server, and a network server. In some implementations, computingdevice 142 may represent a group of computing devices in a server farm.

In an embodiment, control unit 132 may periodically send wellsiteproduction data via a communication network to processing system 140 forprocessing and storage. Such wellsite production data may include, forexample, production system measurements from various downhole devices,as described above. In some implementations, such production data may besent using a remote terminal unit (RTU) of control unit 132. In anembodiment, data storage device 144 may be used to store the productiondata received from control unit 132. In an example, data storage device144 may be used to store historical production data including a recordof actual and simulated production system measurements obtained orcalculated over a period of time, e.g., multiple simulation time-steps,as will be described in further detail below.

While production wells 100A and 100B are described in the context of asingle reservoir, it should be noted that the embodiments disclosedherein are not limited thereto and that the disclosed embodiments may beapplied to fluid production from multiple reservoirs in amulti-reservoir production system with a common surface or gatheringnetwork, as will be described in further detail below with respect toFIG. 3. Thus, a plurality of surface control units similar to controlunit 132 may be used to send production system data from the respectivewellsites of different reservoirs in the production system to processingsystem 140. In addition to the above-described SCADA functionality,processing system 140 may be used to process the received data andsimulate fluid production in the multi-reservoir system, as will bedescribed in further detail below.

FIG. 2 is a block diagram of an exemplary system 200 for simulatingfluid production in a multi-reservoir system. For example, system 200may be used to implement a processing system, e.g., processing system140 of FIGS. 1A and 1B, as described above, for processing wellsite datasent by a surface control unit (e.g., control unit 132 of FIGS. 1A and1B) of a production well associated with each reservoir in theproduction system. As shown in FIG. 2, system 200 includes a reservoirsimulator 210, a memory 220, a user interface (UI) 230 and a networkinterface 240. Reservoir simulator 210 includes a fluid model generator212, a flow simulator 214 and a data presentation unit 216. In anembodiment, reservoir simulator 210 and its components (including fluidmodel generator 212, flow simulator 214 and presentation unit 216),memory 220, UI 230 and network interface 240 may be communicativelycoupled to one another via an internal bus of system 200.

In an embodiment, system 200 can be implemented using any type ofcomputing device having at least one processor and a processor-readablestorage medium for storing data and instructions executable by theprocessor. Examples of such a computing device include, but are notlimited to, a desktop computer, a workstation, a server, a cluster ofcomputers (e.g., in a server farm) or similar type of computing device.Such a computing device may also include an input/output (I/O) interfacefor receiving user input or commands via a user input device (notshown). The user input device may be, for example and withoutlimitation, a mouse, a QWERTY or T9 keyboard, a touch-screen, a graphicstablet, or a microphone. The I/O interface may also include a displayinterface for outputting or presenting information on a display (notshown) coupled to or integrated with the computing device.

While only reservoir simulator 210, memory 220, UI 230 and networkinterface 240 are shown in FIG. 2, it should be appreciated that system200 may include additional components, modules, and/or sub-components asdesired for a particular implementation. It should also be appreciatedthat reservoir simulator 210 and its components may be implemented insoftware, firmware, hardware, or any combination thereof. Furthermore,it should be appreciated that embodiments of reservoir simulator 210, orportions thereof, can be implemented to run on any type of processingdevice including, but not limited to, a computer, workstation, embeddedsystem, networked device, mobile device, or other type of processor orcomputer system capable of carrying out the functionality describedherein.

In an embodiment, system 200 may use network interface 240 tocommunicate with different devices and other systems via a network 204.Network 204 can be any type of network or combination of networks usedto communicate information between different computing devices. Network204 can include, but is not limited to, a wired (e.g., Ethernet) or awireless (e.g., Wi-Fi or mobile telecommunications) network. Inaddition, network 204 can include, but is not limited to, a local areanetwork, medium area network, and/or wide area network such as theInternet.

In an embodiment, system 200 may use network interface 240 to send andreceive information to and from a wellsite control and monitoringdevice, e.g., surface control unit 132 of FIGS. 1A and 1B, as describedabove, via network 204. Such information may include, for example,production system data sent from the wellsite control and monitoringdevice to system 200 via network 204. Likewise, various control signalsand commands may be sent by system 200 to the wellsite control andmonitoring device via network 204, e.g., for purposes of controllingwellsite operations or requesting wellsite production system data fromthe device. In some implementations, such control signals may be in theform of telemetry signals sent using a telemetry transceiver integratedwithin network information 240 of system 200.

In an embodiment, the control signals or commands sent by system 200 tothe device at the wellsite may be based on input received from a user202 via UI 230. User 202 may interact with UI 230 via a user inputdevice (e.g., a mouse, keyboard, or touch-screen) and a display coupledto system 200 to configure, control or monitor the execution ofproduction system simulation. In accordance with user input received byreservoir simulator 210 via UI 230, production system data may berequested and received from a wellsite control and monitoring device vianetwork 204, as described above. The data received from the device maybe processed and used by reservoir simulator 210 in the productionsystem simulation. The results of the simulation may then be presentedby presentation unit 216 to user 202 via UI 230.

In an embodiment, memory 220 may be used to store the production systemdata from the device in the above example in addition to various othertypes of data accessible by reservoir simulator 210 and its components(including fluid model generator 212, flow simulator 214 andpresentation unit 216) for implementing the production system simulationfunctionality disclosed herein. Memory 220 can be any type of recordingmedium coupled to an integrated circuit that controls access to therecording medium. The recording medium can be, for example and withoutlimitation, a semiconductor memory, a hard disk, or similar type ofmemory or storage device. In some implementations, memory 220 may be aremote cloud-based storage location accessible to system 200 via networkinterface 240 and network 204.

In the example shown in FIG. 2, the data stored in memory 220 mayinclude production data 222, fluid data 224 and simulation data 226. Aswill be described in further detail below, reservoir simulator 210 mayuse a combination of production data 222, fluid data 224 and simulationdata 226 to derive a desired set of operating points for a giventime-step of the production system simulation.

Production data 222 may include, for example, actual and/or simulatedproduction system measurements. Actual production system measurementsmay include, for example, surface and downhole well measurements fromvarious production wells in the multi-reservoir system. Suchmeasurements may include, but are not limited to, pressure, temperatureand fluid flow measurements taken downhole near the well perforations,along the production string, at the wellhead and within the gatheringnetwork prior to the point where the fluids mix with fluids from otherreservoirs. Likewise, the simulated measurements may include, forexample and without limitation, estimates of pressure, temperature andfluid flow. Such estimates may be determined based on, for example,simulation results from one or more previous time-steps.

Fluid data 224 may represent different reservoir fluid components (e.g.,heavy crude, light crude, methane, etc.) and related propertiesincluding, for example, their proportions, fluid density and viscosityfor various compositions, pressures and temperatures, or other data. Inan embodiment, fluid data 224 may include black oil and/or EOS modeldata, e.g., in the form of one or more data tables, representing thefluids of each reservoir within the multi-reservoir production system.

In an embodiment, fluid model generator 212 may generate a fluid modelfor each reservoir in the multi-reservoir system based on correspondingproduction data 222 and fluid data 224. For example, fluid modelgenerator 212 may determine parameters for each fluid component or groupof components of the reservoir based on actual and simulated productionsystem measurements (e.g., from one or more prior simulation time-steps)and fluid component characterizations associated with each reservoir.The resulting model for each component/group can then be applied toknown state variables to calculate unknown state variables at eachsimulation point or “gridblock” within the reservoir, at the wellboreperforations or “sandface,” and within the common gathering network ofthe production system. These unknown variables may include, for exampleand without limitation, each gridblock's liquid volume fraction,solution gas-oil ratio and formation volume factor.

In an embodiment, the resulting fluid component state variables, bothmeasured and calculated, may be provided as inputs to flow simulator 214for simulating the flow of fluids through the multi-reservoir productionsystem. Additional inputs to flow simulator 214 may include, forexample, various floating parameters, fixed parameters andcharacterization data related to the production system and constraintsthereof. The floating parameters may include, for example, variousenhanced oil recovery (EOR) parameters including, but not limited to,gas lift injection rates, reservoir gas injection rates and reservoirliquid injection rates. Examples of fixed parameters may includefacility constraints (e.g., a production capacity limit) and defaultproduction rates for individual wells. Reservoir characterization datamay include, for example, geological data describing reservoirformations (e.g., log data previously collected during drilling and/orprior logging of the well) and formation characteristics (e.g.,porosity). The above-described fluid component state variables alongwith the other simulation inputs, parameters and production systemconstraints may be stored in memory 220 as simulation data 226.

In an embodiment, flow simulator 214 may employ set of a fully-coupledequations to perform the simulation and determine optimal operatingsettings for the production system such that production of thereservoirs can be maximized over time without exceeding any facilityconstraints. The equations are characterized as “fully-coupled” becauseall the equations for all the reservoirs and the gathering network maybe solved simultaneously, rather than solving the reservoir andgathering network separately and iterating between the reservoir andgathering network solutions to determine appropriate boundary conditionsfor each set of equations (i.e., loosely-coupled).

In an embodiment, the fully-coupled equations may be used with any ofvarious numerical analysis techniques (e.g., a Newton-Raphson method) todetermine a set of mass and/or volume balance values for each gridblock.The equations also may be used to determine the flow of fluids throughthe production system and provide a solution that includes operatingsettings that honor the various production system constraints, e.g., oneor more facility constraints, gathering network constraints, wellconstraints, or reservoir constraints. Further, the equations may beused by flow simulator 214 to determine updated fluid properties (e.g.,updated fluid component mass and volume values for each gridblock) atthe end of the simulation time-step. At least some of the updatedparameters may be provided, for example, as previous time-step data forsubsequent simulation time-steps. In addition, the simulation performedby flow simulator 214 may be repeated for each of a plurality ofdifferent time-steps, where the simulation results for a given time-stepare used to update the simulation for the next time-step.

With the state of the fluids known throughout the production system, theflow of fluid can be simulated using mass/volume balance equationsrepresentative of the reservoir, of perforations in the wellbore and ofthe gathering network. In an embodiment, the facility equationsrepresenting the gathering network include molar balance equations atthe nodes, hydraulic equations, constraint equations, and compositionequations. The independent variables for the facility equations includepressure and composition for the nodes, and molar flow rates for theconnections.

The full system of equations can be expressed as follows:

$\begin{matrix}{{\begin{bmatrix}A_{rr} & \; & A_{rf} \\\; & A_{pp} & A_{pf} \\\; & A_{fp} & A_{ff}\end{bmatrix}\begin{bmatrix}{\delta\; x_{r}} \\{\delta\; x_{p}} \\{\delta\; x_{f}}\end{bmatrix}} = {- \begin{bmatrix}R_{r} \\R_{p} \\R_{f}\end{bmatrix}}} & (1)\end{matrix}$

where R denotes the residuals, and A the Jacobian for a Newton iterationof the production system simulation. A contains the derivatives of theresiduals with respect to the variables x, where x_(r) includesgridblock moles and pressures, x_(p) includes perforation flow rates,and x_(f) includes facility node compositions and pressures and thetotal molar flow rate of the facility connections. The first row ofequations represents the reservoir equations (simulating fluid flowthrough the reservoir), the second row represents the perforationequations (simulating fluid flow through the perforations), and thethird row represents the facility equations (simulating fluid flowthrough the gathering network).

In an embodiment, the reservoir equations include molar balanceequations of the form:R _(ri) =F _(i) ^(in) −F _(i) ^(out) −a _(i) +G _(i)−Σ_(pεp) _(r) Q_(rpi)  (2)

where the residual R_(ri) of component i for each reservoir gridblock ris driven to zero at full convergence of the equations. For component i,F_(i) ^(in) and F_(i) ^(out) are the molar flow rates across reservoirgridblock faces, a_(i) is the rate of accumulation, G_(i) is the rate ofgeneration and Q_(rpi) is the perforation flow rate (positive forproduction, negative for injection) between a reservoir gridblock r anda wellbore through perforation p. The Q_(rpi) are summed over theperforations within gridblock r. The independent variables are the mass(in moles) of each component i, and the gridblock pressure. In additionto the molar balance equations, in at least some illustrativeembodiments a volume balance equation operates to constrain the porevolume so that it equals the fluid volume. This can be written inresidual form as:R _(r,nc) _(r) ₊₁ =V _(Pr) −V _(Fr)  (3)

where nc_(r) is the number of reservoir pseudo-components, V_(Pr) is thepore volume and V_(Fr) is the fluid volume for gridblock r.

In at least some illustrative embodiments, the perforation equations areexpressed as flow equations for each perforation within a reservoirgridblock. Starting with the simpler case of a single reservoir and agathering network with the same number of pseudo-components, theperforation equation for producing perforations can be expressed usingthe flow equation,

$\begin{matrix}{Q_{rpi} = {C_{p}{\Delta\Phi}_{p}{\sum_{m = 1}^{Nphases}{\frac{{krel}_{rm}}{\mu_{rm}}\rho_{rm}z_{rmi}}}}} & (4)\end{matrix}$

where Q_(rpi) is the perforation flow rate of fluid pseudo-component ifor perforation p within gridblock r, C_(p) is the wellbore constant(equal to the well index multiplied by the permeability-thicknessproduct), ΔΦ_(p) is the permeability-thickness product (i.e., thepotential difference from the reservoir to the wellbore for perforationp), and where for phase m within gridblock r, krel_(rm) is the relativepermeability, μ_(rm) is the viscosity, ρ_(rm) is the density, andz_(rmi) is the mole fraction of fluid pseudo-component i. Similarly, theperforation equation for injecting perforations can be expressed usingthe flow equation,Q _(rpi) =C _(p)λ_(p) ^(inj)ρ_(p) ^(inj)ΔΦ_(p) z _(rpi)  (5)

where λ_(p) ^(inj) is the fluid mobility (e.g., the sum of the gridblockphase mobilities or an endpoint mobility), ρ_(p) ^(inj) is theperforation-injected fluid density, and z_(rpi) is the component molefraction at a node in the wellbore.

The above-described simulation assumes a configuration of the productionsystem in which multiple reservoirs are coupled to a common surface orgathering network. Such a gathering network may include, for example, aplurality of nodes with connections between the nodes and variousreservoir gridblocks. Nodes may represent physical locations of relevantcomponents or devices (e.g., separator 310 of FIG. 3, as will bedescribed below) within the gathering network and/or the productionwells of various reservoirs. Connections may represent pipes or flowcontrol devices, for example, pumps, compressors, valves, or similartypes of devices. An example of such a production system configurationis shown in FIG. 3.

FIG. 3 is a diagram illustrating an exemplary multi-reservoir systemincluding a common surface or gathering network. As shown in FIG. 3, agroup of N reservoirs 302-1 through 302-N are coupled together through agathering network 320. Individual well lines 304 (1 through N) from eachwell couple to a corresponding reservoir node 306 (1 through N), witheach node coupling through a reservoir line 305 (1 through N) to acommon node 308. Common node 308 may provide, for example, mixed fluidsproduced from reservoirs 302-1 to 302-N through riser 309 to aprocessing facility 300. The mixed fluids that are produced at commonnode 308 through riser 309 may include fluids produced from any numberof reservoirs 302-1 to 302-N, for example, all of the reservoirs or anysubset thereof. In the example shown, processing facility 300 includes aseparator 310 that receives the mixed product from facility riser 309and separates the product into water, oil and gas. These separatedproducts are respectively stored in water storage 312, oil storage 316and gas storage 314 for later use and/or delivery further downstream(e.g., to a refining facility). Alternatively, some of the separatedproduct may be used to assist with the removal of product from thereservoir. For example, a portion of the separated gas and/or water maybe reinjected into one or more reservoirs as part of an enhanced oilrecovery (EOR) operation, as indicated by the dashed arrows in FIG. 3.

Maximizing fluid production in the multi-reservoir production system ofFIG. 3 may involve controlling the production of each individual wellsuch that the combined production of the wells, or a selected group ofthe wells, provides the greatest possible amount of hydrocarbon (e.g.,oil and/or gas) production within the operating limits of processingfacility 300 and without exceeding any production system constraints. Inan embodiment, optimal well operating points that maximize fluidproduction over time and enable processing facility 300 to operatewithin its limits may be determined from the results of a simulation offluid production in the multi-reservoir system. For example, reservoirsimulator 210 of FIG. 2, as described above, may be used to identify theoptimal well operating points from a simulation of fluid production inthe multi-reservoir system of FIG. 3 based on production systemmeasurements, reservoir characterizations and constraints related toreservoirs 302-1 to 302-N and processing facility 300. In someimplementations, such operating points may be expressed as a solution toa simultaneous set of fully-coupled equations, as described above.

In addition to using simulation results to determine optimal welloperating points and maximize fluid production in the multi-reservoirsystem, a reservoir engineer (e.g., user 202 of reservoir simulator 210of FIG. 2) might be interested in improving the computational efficiencyof the simulation itself and the accuracy of the simulation results. Aswill be described in further detail below, the fluid modeling andproduction simulation techniques disclosed herein may allow suchimprovements to be achieved for the simulation by using a modified orsimplified compositional model representing the mixed fluids producedfrom the multiple reservoirs of the above-described multi-reservoirproduction system.

Referring to FIG. 3, each of reservoirs 302-1 to 302-N may be associatedwith an EOS model, e.g., in the form of one or more EOS data tables,representing the fluids within that reservoir. In this example, eachreservoir may have at least two fluid components, e.g., an oil componentand a gas component, which can be produced into gathering network 320.In an embodiment, it may be assumed for purposes of the simulation thatthe gas components of reservoirs 302-1 to 302-N are identical while theoil components maintain their separate identities for each reservoir.However, it should be noted that the gas components also may retaintheir separate identities in some implementations, e.g., for moreflexibility when dealing with condensate reservoirs.

As will be described in further detail below with respect to the processshown in FIG. 4, an EOS characterization of reservoir fluids may be usedto produce an EOS model for each of a plurality of reservoirs within themulti-reservoir system. Additionally, some of the reservoir fluids maybe represented by black oil models. In this case, an additional step isrequired to transform the black oil models to EOS models so that foreach reservoir the basis of the mixing will be the mixing of EOS models.While the EOS models for the reservoirs in the multi-reservoir systemmay have at least some light components in common, the disclosedembodiments do not require the EOS model for each reservoir to have thesame EOS components as in conventional approaches, which utilize amaster EOS model with a component set that can be expanded from thecomponent sets of individual reservoirs. The EOS model in accordancewith the disclosed embodiments may have at least one heavy component,also referred to herein as a “marker component,” that is unique to eachreservoir in the multi-reservoir system. Further, while the embodimentsdisclosed herein may perform delumping, the delumping is performedwithout using full EOS modeling for calculating the fluid properties ofthe mixed fluid. Instead, the disclosed embodiments use the markercomponent to generate property tables as a function of pressure and themarker component.

FIG. 4 is a flowchart of an exemplary method 400 of using a simplifiedcompositional model for determining the properties of mixed fluidsproduced in a multi-reservoir system having a common surface network.For discussion purposes, method 400 will be described using theabove-described multi-reservoir system of FIG. 3 but is not intended tobe limited thereto. As shown in FIG. 4, method 400 includes steps 402,404, 406, 408, 410, 412 and 414. However, it should be noted that method400 may include additional steps to perform the techniques disclosedherein, as desired for a particular implementation. The steps of method400 may be implemented by, for example, reservoir simulator 210 of FIG.2, as described above, but method 400 is not intended to be limitedthereto.

Method 400 begins in step 402, which includes matching an EOScharacterization of fluids with an EOS model for each of a plurality ofreservoirs (e.g., reservoirs 302-1 to 302-N of FIG. 3) in themulti-reservoir system. The EOS model for each reservoir may representdifferent components of the reservoir's fluids in their EOS form. Thedifferent fluid components represented by the EOS model for eachreservoir may include, for example, one or more light fluid componentsthat are common across all of the reservoirs in the production systemand at least one heavy fluid component (or marker component) that isunique to each reservoir. In an embodiment, the common light fluidcomponents may be gas components and the unique heavy oil component maybe an oil component.

Method 400 then proceeds to step 404, in which fluid production in themulti-reservoir system may be simulated at different points in thecommon surface network. The simulation points may correspond to, forexample, different nodes (e.g., nodes 306-1 to 306-N of FIG. 3) in thecommon surface network, as described above. In step 406, it isdetermined for each simulation point in step 404 whether there are mixedfluids produced from different reservoirs. In step 408, the results ofthe determination in step 406 may be used to decide whether method 400will proceed to step 410 or step 412. In an example, if it is determinedin step 406 that the fluids at a particular simulation point in thenetwork are produced from only a single reservoir, method 400 mayproceed from step 408 to step 410. Step 410 may include calculatingfluid properties using the EOS model (from step 402) corresponding tothe reservoir from which the fluids are produced.

Alternatively, if it is determined in step 406 that there is acommingling or mixing of fluids from different reservoirs at theparticular simulation point in question, method 400 may proceed fromstep 408 to step 412. Step 412 includes generating one or moreinterpolation tables representing the mixed fluids produced from thedifferent reservoirs via the common surface network, based on thecorresponding EOS model for each of the different reservoirs. In anembodiment, the interpolation tables may be generated as a function ofpressure and the marker components of the different reservoirs in thisexample. In step 414, the generated interpolation tables from step 412may be used to calculate the properties of the mixed fluids.

In an embodiment, the mixed fluid properties may be calculated byperforming a table look-up using any of various table look-up techniquesbased on the marker component and the one or more interpolation tables.A benefit of using such table look-up techniques is that theinterpolation tables generated in step 412 may be used in place of phaseequilibrium calculations, which can often be the most expensive part ofphase behavior calculations for the mixed fluids. For example, theinterpolation tables may provide a simplified compositional model thatcan be used to replace the equilibrium flash calculations associatedwith conventional phase behavior calculation techniques. The phasesplits of the mixed fluids may be determined using such a simplifiedflash replacement, and a standard EOS based table look-up technique maybe used to calculate the individual phase properties. Examples of suchEOS flash replacement techniques that may be used include, but are notlimited to:

1) use of compositionally dependent K-value calculations in whichK-values are tabulated as functions of pressure, temperature and thecomposition of one or more components including, for example, one ormore marker components, and the K-values are used to determine the phaseequilibrium while phase properties are calculated using standard EOStechniques;

2) use of EOS interpolation techniques for simplified phase equilibriumcalculations in which saturation pressure, K-values at the saturationpressure, liquid and vapor compressibility factors are tabulated asfunctions of pressure, temperature, and the composition of some of thecomponents including the marker components while EOS calculations may beused to determine other fluid properties; and

3) use of the interpolation tables as a function of compositionaltie-lines relating oil and gas compositions.

In the latter technique, the phase mole fractions and the phasecompositions may be tabulated as a function of pressure, temperature andoverall component compositions. Then, standard EOS techniques may beused to calculate other phase properties. Some or all of the componentsmay be included, e.g., as interpolants, in addition to all of the markercomponents.

The disclosed embodiments will now be further described using thefollowing examples of possible data structures that may be defined forthe individual reservoirs and for the mixed fluid models describedabove.

For example, Table 1 below shows the black oil data for a reservoir 1.The black oil data in Table 1 may provide, for example, a black oilmodel description of the fluids in reservoir 1, which can be input to areservoir simulator, e.g., reservoir simulator 210 of FIG. 2, asdescribed above, for performing the simulation. As shown in Table 1below, the main data columns of this input table may be for the pressure(in psia), the solution gas oil ratio (Rs) in units of MSCF/STB, and theoil formation volume factor (Bo) in units of STB/RB. While only thesethree main columns are shown below, it should be noted that embodimentsare not intended to be limited thereto and that the table may haveadditional columns representing other black oil data parametersincluding, for example and without limitation, gas FVF, solution gas-oilratio, oil viscosity, and gas viscosity. Additionally, undersaturateddata may be associated with at least one of the pressures.

TABLE 1 Original Black Oil Model Data for Reservoir 1 Pressure (psia) Rs(MSCF/STB) Bo (STB/RB) 3000 1.2 1.3 2000 0.8 1.2 1000 0.4 1.1 14.70.00001 1

Table 2 below shows the fluid component properties for a reservoir 2, asrepresented by an EOS model of the reservoir's fluids. As shown below,Table 2 may include data columns for key fluid properties of interestduring the simulation including, for example and without limitation,molecular weight, critical temperature, and critical pressure. It shouldbe noted that the table for a full EOS model description may includeadditional columns of data, for example, columns for an acentric factor,critical volume, parachors, and volume translation factors for eachcomponent. A separate table of interaction coefficients may also beused.

TABLE 2 EOS Model Data for Reservoir 2 Critical Critical pressureComponent Name Molecular weight temperature (R) (psia) P1 18 300 550 P235 549.8 850 P3 44.1 665.7 616.3 P4 65 820 485 P5 90 950. 438. P6 2001200 255

In an embodiment, an EOS characterization of the mixed fluids fromdifferent reservoirs may be generated. Such an EOS characterization mayinclude, for example, a set of light components (CO2, N2, H2S, C1, C2,C3, iC4, nC4, iC5, nC5, C6) and a set of common heavy components (HC1,HC2), two heavy component(s) exclusive to reservoir 1 (R1H1 and R1H2),and at least one heavy component that is exclusive to a reservoir 2(R2H1). The actual number of light components may be the same, but thenumber of heavy components may be specific to a particularimplementation. The process performs a characterization procedure asdescribed in step 1 above.

In one example, a common EOS model may be generated based on a set ofreservoir pseudo-components defined using common pseudo-components thatoverlap between reservoirs 1 and 2. Each of the “pseudo-components” mayrepresent, for example, any number of real fluid components that aregrouped together or “lumped” into a single component that can beprocessed as an individual unit. The use of overlapping commonpseudo-components may enable the application of fully-coupledmass/volume balance equations to multiple reservoirs, wells and thegathering network using a larger but still relatively small number ofcommon pseudo-components (e.g., less than the total of all the reservoirpseudo-components). The common pseudo-components represent a commonfluid characterization that includes sufficient components to representthe behavior of multiple fluids in different reservoirs.

The common characterization in this example may be based on anexpression of the components as common pseudo-components that aredefined based on the components' bulk hydrocarbon composition or carbonnumber up to C45. The set of light components CO2, N2, H2S, CI, C2, C3,nC4, iC4, nC5, iC5 and C6 may be used with their commonly acceptedproperties, while the C7+ heavy components are defined using aprobability distribution function that provides the molecular weight andmole fraction for each carbon number from C7 to C45. It should beappreciated that any of various techniques may be used to define the C7+heavy components. Once a set of molecular weights and mole fractions areestablished, a Watson or other type of characterization factor may becalculated for each common pseudo-component, which in turn may be usedto calculate the specific gravity of each common pseudo-component. Itshould be appreciated that any of various techniques may be used tocalculate pseudo-component specific gravities and/or otherpseudo-component characteristics will become apparent to those ofordinary skill in the art, and all such techniques are within the scopeof the present disclosure. The true boiling point (TBP) for each commonpseudo-component may also be calculated. The molecular weights, TBPs andspecific gravities can be combined using any of a number of correlationtechniques to calculate the critical properties needed by the fluidmodels. Non-zero interaction coefficients may also need to be estimatedthrough correlations.

At this stage, a large number of pseudo-components may be used, farlarger than the usual number normally used to simulate reservoir andnetwork systems. In order to improve computational efficiency, thecomponents are lumped together in a pseudoization process. For example,in this case the heavy components of reservoir larc lumped together intotwo pseudo-components R1H1 and R1H2, while the heavy components ofreservoir 2 are lumped together into pseudo-component R2H1.

The critical properties and the interaction coefficients generated inthe above manner may need to be adjusted to adequately match the fluidproperties for each reservoir. Regression methods may be applied toadjust the values of the fluid parameters.

For calculations of commingled fluids, the extra step of mixing orweaving components from the two EOS models is carried out. In this case,the two EOS models share the same light components and light componentproperties so only the compositions of the light components in themixture needs to be adjusted. Using simple weaving, we retain the heavycomponents R1H1 and R1H2 from reservoir 1, and R2H2 from reservoir 2.The mole fractions are adjusted during the mixing step.

The EOS model based on the above-described fluid componentcharacterizations may be represented using a data table similar to Table3 below, which may be provided as input to a reservoir simulator (e.g.,reservoir simulator 210 of FIG. 2, as described above).

TABLE 3 EOS Model Data for Mixed Fluids from Different ReservoirsCritical Critical pressure Component Name Molecular weight temperature(R) (psia) CO2 44.01 547.6 1070.9 N2 28.01 227.3 493.0 H2S 34.08 212.71036.0 C1 16.043 343.0 667.8 C2 30.07 549.8 707.8 C3 44.1 665.7 616.3iC4 58.12 734.7 529.1 nC4 58.12 765.3 550.7 iC5 72.15 828.8 490.4 nC572.15 845.4 488.6 C6 86.18 913.4 436.9 HC1 98.55 1004.4 441.5 HC2 319.831490.2 191.1 R1H1 135.84 1135.1 362.7 R1H2 206.25 1309.6 266.9 R2H1500.0 1670.4 140.3

The EOS model may then be used to generate tables of compositionalcalculations in which values are tabulated as a function of pressure,temperature and compositions, e.g., in the form of one or moreinterpolation parameters. It is possible that only some of thecomponents may be required as interpolants, in which case the markercomponents may be targeted to be the interpolants. For example, in oneembodiment, in the above-described example of mixed fluids from twodifferent reservoirs, at least one of the unique “marker” componentsthat is specific to one of the reservoirs, e.g., R1H1, may be selectedas the interpolation parameter. Tables 4-6 below are examples ofgenerated tables for different values of the interpolation parameter,e.g., the marker component (R1H1) in this example. The composition ofthe respective oil and gas phases in each table may be expressed, forexample, as a list of 15 numbers corresponding to the values of theremaining components, i.e., excluding R1H1, as shown in Table 3 above.Depending on the calculation mechanism, alternative data such asK-values or phase Z-factors may be tabulated instead of the compositionsand phase fractions.

TABLE 4 Mole fraction R1H1 = 0 Composition of gas Composition of oilphase (a list of phase (a list of Fraction of 15 numbers 15 numbersPressure gas phase that sum to 1.) that sum to 1.) 3000 0.01 (0.02,0.005, 0.0005, (0.002, 0.0003, 0.52, . . . , 0.015) 0.00003, 0.2, . . ., 0.125) 2000 0.22 (0.02, 0.0048, (0.002, 0.0003, 0.00052, 0.533, . . ., 0.00003, 0.19, . . . , 0.0166) 0.135) 1000 0.35 (0.02, 0.0048, (0.002,0.0003, 0.00052, 0.543, . . . , 0.00003, 0.19, . . . , 0.0173) 0.127)

TABLE 5 Mole fraction R1H1 = 0.5 Composition of gas Composition of oilphase (a list of phase (a list of Fraction of 15 numbers 15 numbersPressure gas phase that sum to 1) that sum to 1.) 3000 0.015 (0.02,0.005, 0.0005, (0.002, 0.0003, 0.52, . . . , 0.015) 0.00003, 0.2, . . ., 0.125) 2000 0.27 (0.02, 0.0048, (0.002, 0.0003, 0.00052, 0.533, . . ., 0.00003, 0.19, . . . , 0.0166) 0.135) 1000 0.37 (0.02, 0.0048, (0.002,0.0003, 0.00052, 0.543, . . . , 0.00003, 0.19, . . . , 0.0173) 0.127)

TABLE 6 Mole fraction R1H1 = 1.0 Composition of gas Composition of oilphase (a list of phase (a list of Fraction of 15 numbers 15 numbersPressure gas phase that sum to 1.) that sum to 1.) 3000 0.020 (0.02,0.005, 0.0005, (0.002, 0.0003, 0.52, . . . , 0.015) 0.00003, 0.2, . . ., 0.125) 2000 0.32 (0.02, 0.0048, (0.002, 0.0003, 0.00052, 0.533, . . ., 0.00003, 0.19, . . . , 0.0166) 0.135) 1000 0.41 (0.02, 0.0048, (0.002,0.0003, 0.00052, 0.543, . . . , 0.00003, 0.19, . . . , 0.0173) 0.127)

In the above example, other interpolation parameters may be added formixed fluids from different reservoirs, for example, a unique or specialmarker component of reservoir 2, e.g. R2H1.

Accordingly, the disclosed embodiments provide a method for usingsimplified compositional models for calculating properties of mixedfluids in a common surface network. In contrast to the disclosedembodiments, other approaches with different EOS models and black oilmodels tied to a common network all try to match the fluid behavior to asingle equation of state model using a common set of components.Additionally, the disclosed embodiments also match to EOS models, butwithout the requirement that all components must exist within eachfluid. Further, while other approaches then delump the fluids to thecommon EOS model at the sandface, the disclosed embodiments areconfigured to generate a multi-dimension interpolation table with aspecial marker component from each reservoir as a parameter. Thesetables are used in an approach which will replace the EOS equilibriumcalculations.

Thus, advantages of the disclosed embodiments over prior methods includeincreased computational efficiency and in some cases, more accuracy.Additionally, in parts of the reservoir that have no commingling offluids from different reservoirs, the original EOS data may be used.Alternatively, the simplified table lookup technique may also be usedeverywhere for computational efficiency and consistency. In parts of thenetwork where mixed fluids are produced from different reservoirs, thedisclosed embodiments may be used as a basis for calculating theproperties of the mixed fluids that are produced from the mixing of EOSfluids from different reservoirs in different proportions. The disclosedembodiments allow the operators to keep their original EOScharacterization, thereby providing a relatively efficient way forcalculating properties of mixed fluids in the common network.

FIG. 5 is a block diagram of an exemplary computer system 500 in whichembodiments of the present disclosure may be implemented. For example,system 500 may be used to implement system 200 of FIG. 2, as describedabove. The system 500 may be any type of computing device including, butnot limited to, a desktop computer, a laptop, a server, a tablet, and amobile device. The system 500 includes, among other components, aprocessor 510, main memory 502, secondary storage unit 504, aninput/output interface module 506, and a communication interface module508.

The processor 510 may be any type or any number of single core ormulti-core processors capable of executing instructions for performingthe features and functions of the disclosed embodiments. Theinput/output interface module 506 enables the system 500 to receive userinput (e.g., from a keyboard and mouse) and output information to one ormore devices such as, but not limited to, printers, external datastorage devices, and audio speakers. The system 500 may optionallyinclude a separate display module 511 to enable information to bedisplayed on an integrated or external display device. For instance, thedisplay module 511 may include instructions or hardware (e.g., agraphics card or chip) for providing enhanced graphics, touchscreen,and/or multi-touch functionalities associated with one or more displaydevices.

Main memory 502 is volatile memory that stores currently executinginstructions/data or instructions/data that are prefetched forexecution. The secondary storage unit 504 is non-volatile memory forstoring persistent data. The secondary storage unit 504 may be orinclude any type of data storage component such as a hard drive, a flashdrive, or a memory card. In one embodiment, the secondary storage unit504 stores the computer executable code/instructions and other relevantdata for enabling a user to perform the features and functions of thedisclosed embodiments.

For example, in accordance with the disclosed embodiments, the secondarystorage unit 504 may permanently store executable code/instructions 520for performing the steps of method 400 of FIG. 4, as described above.The executable code/instructions 520 are then loaded from the secondarystorage unit 504 to main memory 502 during execution by the processor510 for performing the disclosed embodiments. In addition, the secondarystorage unit 504 may store other executable code/instructions and data522 such as, but not limited to, a reservoir simulation application(e.g., a reservoir simulation application) for use with the disclosedembodiments.

The communication interface module 508 enables the system 500 tocommunicate with the communications network 530. For example, thenetwork interface module 508 may include a network interface card and/ora wireless transceiver for enabling the system 500 to send and receivedata through the communications network 530 and/or directly with otherdevices.

The communications network 530 may be any type of network including acombination of one or more of the following networks: a wide areanetwork, a local area network, one or more private networks, theInternet, a telephone network such as the public switched telephonenetwork (PSTN), one or more cellular networks, and/or wireless datanetworks. The communications network 530 may include a plurality ofnetwork nodes (not depicted) such as routers, network accesspoints/gateways, switches, DNS servers, proxy servers, and other networknodes for assisting in routing of data/communications between devices.

For example, in one embodiment, the system 500 may interact with one ormore servers 534 or databases 532 for performing the features of thedisclosed embodiments. For instance, the system 500 may query thedatabase 532 for well log information for creating a reservoir model inaccordance with the disclosed embodiments. Further, in certainembodiments, the system 500 may act as a server system for one or moreclient devices or a peer system for peer to peer communications orparallel processing with one or more devices/computing systems (e.g.,clusters, grids).

As described above, embodiments of the present disclosure areparticularly useful for calculating properties of mixed fluids producedin a multi-reservoir system with a common surface network. In oneembodiment of the present disclosure, a computer-implemented method ofsimulating fluid production in a multi-reservoir system with a commonsurface network includes: matching equation of state (EOS)characterization of fluids with a delumped EOS model for each of aplurality of reservoirs within the multi-reservoir system, where thedelumped EOS model represents different components of the fluids foreach reservoir; simulating fluid production in the multi-reservoirsystem for at least one simulation point in the common surface network,based in part on the delumped EOS model for each of the plurality ofreservoirs; determining whether or not fluids produced during thesimulation at the simulation point are mixed fluids from differentreservoirs in the plurality of reservoirs; when the fluids at thesimulation point are determined not to be mixed fluids produced fromdifferent reservoirs in the plurality of reservoirs, calculatingproperties of the fluids using the delumped EOS model corresponding toone of the plurality of reservoirs from which the fluids are produced;and when the fluids at the simulation point are determined to be mixedfluids produced from different reservoirs, generating one or moreinterpolation tables representing the mixed fluids produced from thedifferent reservoirs via the common surface network, based on thecorresponding delumped EOS model for each of the different reservoirsand calculating properties of the mixed fluids based on the one or moreinterpolation tables.

In a further embodiment, the one or more interpolation tables includecompositional values that are tabulated as a function of one or moreinterpolation parameters. In yet a further embodiment, the differentfluid components represented by the delumped EOS model for eachreservoir include at least one heavy fluid component that is unique tothat reservoir. In yet a further embodiment, the different fluidcomponents further include at least one light fluid component that iscommon amongst the plurality of reservoirs. In yet a further embodiment,the heavy fluid component is a unique heavy oil component and the lightfluid component is a common gas component. In yet a further embodiment,the one or more interpolation tables represent the fluids of each of theplurality of reservoirs in proportion to the unique heavy oil componentof each reservoir relative to the reservoir's fluid pressure. In yet afurther embodiment, the one or more interpolation tables are used inplace of phase equilibrium calculations for the mixed fluids, andcalculating properties of the mixed fluids comprises performing a tablelook-up of fluid properties using the one or more interpolation tables.In yet a further embodiment, the table look-up is performed using atleast one of a set of tabulated compositionally dependent K-values, anEOS interpolation, or compositional tie-lines relating to oil and gascompositions of the mixed fluids.

In another embodiment of the present disclosure, a system for definingnon-linear petrofacies for a reservoir simulation model includes atleast one processor and a memory coupled to the processor hasinstructions stored therein, which when executed by the processor, causethe processor to perform functions, including functions to: matchequation of state (EOS) characterization of fluids with a delumped EOSmodel for each of a plurality of reservoirs within the multi-reservoirsystem, the delumped EOS model representing different components of thefluids for each reservoir; simulate fluid production in themulti-reservoir system for at least one simulation point in the commonsurface network, based in part on the delumped EOS model for each of theplurality of reservoirs; determine whether or not fluids produced duringthe simulation at the simulation point are mixed fluids from differentreservoirs in the plurality of reservoirs; when the fluids at thesimulation point are determined not to be mixed fluids produced fromdifferent reservoirs in the plurality of reservoirs, calculateproperties of the fluids using the delumped EOS model corresponding toone of the plurality of reservoirs from which the fluids are produced;and when the fluids at the simulation point are determined to be mixedfluids produced from different reservoirs, generate one or moreinterpolation tables representing the mixed fluids produced from thedifferent reservoirs via the common surface network, based on thecorresponding delumped EOS model for each of the different reservoirsand calculate properties of the mixed fluids based on the one or moreinterpolation tables.

In yet another embodiment of the present disclosure, a computer-readablestorage medium has instructions stored therein, which when executed by acomputer cause the computer to perform a plurality of functions,including functions to: match equation of state (EOS) characterizationof fluids with a delumped EOS model for each of a plurality ofreservoirs within the multi-reservoir system, the delumped EOS modelrepresenting different components of the fluids for each reservoir;simulate fluid production in the multi-reservoir system for at least onesimulation point in the common surface network, based in part on thedelumped EOS model for each of the plurality of reservoirs; determinewhether or not fluids produced during the simulation at the simulationpoint are mixed fluids from different reservoirs in the plurality ofreservoirs; when the fluids at the simulation point are determined notto be mixed fluids produced from different reservoirs in the pluralityof reservoirs, calculate properties of the fluids using the delumped EOSmodel corresponding to one of the plurality of reservoirs from which thefluids are produced; and when the fluids at the simulation point aredetermined to be mixed fluids produced from different reservoirs,generate one or more interpolation tables representing the mixed fluidsproduced from the different reservoirs via the common surface network,based on the corresponding delumped EOS model for each of the differentreservoirs and calculate properties of the mixed fluids based on the oneor more interpolation tables.

While specific details about the above embodiments have been described,the above hardware and software descriptions are intended merely asexample embodiments and are not intended to limit the structure orimplementation of the disclosed embodiments. For instance, although manyother internal components of the system 500 are not shown, those ofordinary skill in the art will appreciate that such components and theirinterconnection are well known.

In addition, certain aspects of the disclosed embodiments, as outlinedabove, may be embodied in software that is executed using one or moreprocessing units/components. Program aspects of the technology may bethought of as “products” or “articles of manufacture” typically in theform of executable code and/or associated data that is carried on orembodied in a type of machine readable medium. Tangible non-transitory“storage” type media include any or all of the memory or other storagefor the computers, processors or the like, or associated modulesthereof, such as various semiconductor memories, tape drives, diskdrives, optical or magnetic disks, and the like, which may providestorage at any time for the software programming.

Additionally, the flowchart and block diagrams in the figures illustratethe architecture, functionality, and operation of possibleimplementations of systems, methods and computer program productsaccording to various embodiments of the present invention. It shouldalso be noted that, in some alternative implementations, the functionsnoted in the block may occur out of the order noted in the figures. Forexample, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts, orcombinations of special purpose hardware and computer instructions.

The above specific example embodiments are not intended to limit thescope of the claims. The example embodiments may be modified byincluding, excluding, or combining one or more features or functionsdescribed in the disclosure.

What is claimed is:
 1. A computer-implemented method of simulating amulti-reservoir production system to maximize fluid production, themethod comprising: obtaining, by a computer system via a communicationnetwork, wellsite data for fluids produced from each of a plurality ofreservoirs in a multi-reservoir system having a common surface network;generating an initial fluid model for each of the plurality ofreservoirs, based on the wellsite data obtained for the fluids producedfrom that reservoir; matching an equation of state (EOS)characterization of the initial fluid model generated for each of theplurality of reservoirs with a common EOS model of the multi-reservoirsystem, the common EOS model representing common fluid components forthe plurality of reservoirs and at least one marker component that isunique to each reservoir; simulating fluid production in themulti-reservoir system for different points in the common surfacenetwork, based in part on the common EOS model; determining whether ornot the simulated fluid production for each of the different pointsinclude mixed fluids from different reservoirs, based on the at leastone marker component represented by the common EOS model for each of thedifferent reservoirs; calculating properties of fluids to be produced ateach of the different points in the common surface network, based on thedetermination, wherein: the calculation for each point at which thesimulated fluid production is determined not to include mixed fluids isbased on the initial fluid model for the corresponding reservoir fromwhich the fluids are to be produced; and the calculation for each pointat which the simulated fluid production is determined to include mixedfluids is based on a simplified compositional model that is generated asa function of the at least one marker component represented by thecommon EOS model for each of the different reservoirs from which thefluids are to be produced, wherein the simplified compositional modelincludes one or more interpolation tables of compositional values fordifferent phase properties of the mixed fluids that are tabulated as afunction of one or more interpolation parameters, and the one or moreinterpolation parameters include the at least one marker component thatis unique to each of the different reservoirs from which the mixedfluids are produced; determining operating settings for a selected groupof the plurality of wells corresponding to the different points in thecommon surface network, based on the calculated properties of the fluidsto be produced at each point; and controlling, using control signalstransmitted from the computer system to a wellsite control unit at eachof the selected group of wells via the communication network, productionoperations of the selected group of wells according to the determinedoperating settings.
 2. The method of claim 1, wherein the at least onemarker component for each of the different reservoirs is at least oneheavy fluid component that is unique to that reservoir.
 3. The method ofclaim 2, wherein the different fluid components further include at leastone light fluid component that is common amongst the plurality ofreservoirs.
 4. The method of claim 3, wherein the heavy fluid componentis a unique heavy oil component and the light fluid component is acommon gas component.
 5. The method of claim 4, wherein the one or moreinterpolation tables represent the different fluid components for eachof the different reservoirs in proportion to the unique heavy oilcomponent of that reservoir relative to the reservoir's fluid pressure.6. The method of claim 5, wherein the one or more interpolation tablesare used in place of phase equilibrium calculations for the mixedfluids, and calculating properties of the mixed fluids comprisesperforming a table look-up of fluid properties using the one or moreinterpolation tables.
 7. The method of claim 6, wherein performing thetable look-up comprises using at least one of a set of tabulatedcompositionally dependent K-values, an EOS interpolation, orcompositional tie-lines relating to oil and gas compositions of themixed fluids.
 8. A system comprising: at least one processor; and amemory coupled to the processor having instructions stored therein,which when executed by the processor, cause the processor to performfunctions including functions to: obtain, via a communication network,wellsite data for fluids produced from each of a plurality of reservoirsin a multi-reservoir system having a common surface network; generate aninitial fluid model for each of the plurality of reservoirs, based onthe wellsite data obtained for the fluids produced from that reservoir;match an equation of state (EOS) characterization of the initial fluidmodel generated for each of the plurality of reservoirs with a commonEOS model of the multi-reservoir system, the common EOS modelrepresenting common fluid components for the plurality of reservoirs andat least one marker component that is unique to each reservoir; simulatefluid production in the multi-reservoir system for different points inthe common surface network, based in part on the common EOS model;determine whether or not the simulated fluid production for each of thedifferent points includes mixed fluids from different reservoirs, basedon the at least one marker component represented by the common EOS modelfor each of the different reservoirs; calculate properties of fluids tobe produced at each of the different points in the common surfacenetwork, based on the determination, wherein: the calculation for eachpoint at which the simulated fluid production is determined not toinclude mixed fluids is based on the initial fluid model for thecorresponding reservoir from which the fluids are to be produced; andthe calculation for each point at which the simulated fluid productionis determined to include mixed fluids is based on a simplifiedcompositional model that is generated as a function of the at least onemarker component represented by the common EOS model for each of thedifferent reservoirs from which the fluids are to be produced, whereinthe simplified compositional model includes one or more interpolationtables of compositional values for different phase properties of themixed fluids that are tabulated as a function of one or moreinterpolation parameters, and the one or more interpolation parametersinclude the at least one marker component that is unique to each of thedifferent reservoirs from which the mixed fluids are produced; determineoperating settings for a selected group of the plurality of wellscorresponding to the different points in the common surface network,based on the calculated properties of the fluids to be produced at eachpoint; and control, using control signals transmitted from the computersystem to a wellsite control unit at each of the selected group of wellsvia the communication network, production operations of the selectedgroup of wells according to the determined operating settings.
 9. Thesystem of claim 8, wherein the at least one marker component for each ofthe different reservoirs is at least one heavy fluid component that isunique to that reservoir.
 10. The system of claim 9, wherein thedifferent fluid components further include at least one light fluidcomponent that is common amongst the plurality of reservoirs.
 11. Thesystem of claim 10, wherein the heavy fluid component is a unique heavyoil component and the light fluid component is a common gas component.12. The system of claim 11, wherein the one or more interpolation tablesrepresent the different fluid components for each of the differentreservoirs in proportion to the unique heavy oil component of thatreservoir relative to the reservoir's fluid pressure.
 13. The system ofclaim 12, wherein the one or more interpolation tables are used in placeof phase equilibrium calculations for the mixed fluids, and theproperties of the mixed fluids are calculated by performing a tablelook-up of fluid properties using the one or more fluid property tables.14. The system of claim 13, wherein the table look-up is performed usingat least one of a set of tabulated compositionally dependent K-values,an EOS interpolation, or compositional tie-lines relating to oil and gascompositions of the mixed fluids.
 15. A non-transitory computer-readablestorage medium having instructions stored therein, which when executedby a computer cause the computer to perform a plurality of functions,including functions to: obtain, via a communication network, wellsitedata for fluids produced from each of a plurality of reservoirs in amulti-reservoir system having a common surface network; generate aninitial fluid model for each of the plurality of reservoirs, based onthe wellsite data obtained for the fluids produced from that reservoir;match an equation of state (EOS) characterization of the initial fluidmodel generated for each of the plurality of reservoirs with a commonEOS model of the multi-reservoir system, the common EOS modelrepresenting common fluid components for the plurality of reservoirs andat least one marker component that is unique to each reservoir; simulatefluid production in the multi-reservoir system for different points inthe common surface network, based in part on the common EOS model;determine whether or not the simulated fluid production for each of thedifferent points includes mixed fluids from different reservoirs, basedon the at least one marker component represented by the common EOS modelfor each of the different reservoirs; calculate properties of fluids tobe produced at each of the different points in the common surfacenetwork, based on the determination, wherein: the calculation for eachpoint at which the simulated fluid production is determined not toinclude mixed fluids is based on the initial fluid model for thecorresponding reservoir from which the fluids are to be produced; andthe calculation for each point at which the simulated fluid productionis determined to include mixed fluids is based on a simplifiedcompositional model that is generated as a function of the at least onemarker component represented by the common EOS model for each of thedifferent reservoirs from which the fluids are to be produced, whereinthe simplified compositional model includes one or more interpolationtables of compositional values for different phase properties of themixed fluids that are tabulated as a function of one or moreinterpolation parameters, and the one or more interpolation parametersinclude the at least one marker component that is unique to each of thedifferent reservoirs from which the mixed fluids are produced; determineoperating settings for a selected group of the plurality of wellscorresponding to the different points in the common surface network,based on the calculated properties of the fluids to be produced at eachpoint; and control, using control signals transmitted from the computersystem to a wellsite control unit at each of the selected group of wellsvia the communication network, production operations of the selectedgroup of wells according to the determined operating settings.
 16. Thenon-transitory computer-readable storage medium of claim 15, wherein theat least one marker component for each of the different reservoirs is atleast one heavy fluid component that is unique to that reservoir. 17.The non-transitory computer-readable storage medium of claim 16, whereinthe different fluid components further include at least one light fluidcomponent that is common amongst the plurality of reservoirs.